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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2016

Conceptual Design and Structural Optimization of NASA Environmentally Responsible Aviation (ERA) Hybrid Wing Body Aircraft

Simultaneously achieving the fuel consumption and noise reduction goals set forth by NASA's Environmentally Responsible Aviation (ERA) project requires innovative and unconventional aircraft concepts. In response, advanced hybrid wing body (HWB) aircraft concepts have been proposed and analyzed as a means of meeting these objectives. For the current study, several HWB concepts were analyzed using the Hybrid wing body Conceptual Design and structural optimization (HCDstruct) analysis code. HCDstruct is a medium-fidelity finite element based conceptual design and structural optimization tool developed to fill the critical analysis gap existing between lower order structural sizing approaches and detailed, often finite element based sizing methods for HWB aircraft concepts. Whereas prior versions of the tool used a half-model approach in building the representative finite element model, a full wing-tip-to-wing-tip modeling capability was recently added to HCDstruct, which alleviated the symmetry constraints at the model centerline in place of a free-flying model and allowed for more realistic center body, aft body, and wing loading and trim response. The latest version of HCDstruct was applied to two ERA reference cases, including the Boeing Open Rotor Engine Integration On an HWB (OREIO) concept and the Boeing ERA-0009H1 concept, and results agreed favorably with detailed Boeing design data and related Flight Optimization System (FLOPS) analyses. Following these benchmark cases, HCDstruct was used to size NASA's ERA HWB concepts and to perform a related scaling study.

Quinlan, Jesse R.↗
NASA NTRS2016

Assessment of the Performance Potential of Advanced Subsonic Transport Concepts for NASA's Environmentally Responsible Aviation Project

NASA's Environmentally Responsible Aviation (ERA) project has matured technologies to enable simultaneous reductions in fuel burn, noise, and nitrogen oxide (NOx) emissions for future subsonic commercial transport aircraft. The fuel burn reduction target was a 50% reduction in block fuel burn (relative to a 2005 best-in-class baseline aircraft), utilizing technologies with an estimated Technology Readiness Level (TRL) of 4-6 by 2020. Progress towards this fuel burn reduction target was measured through the conceptual design and analysis of advanced subsonic commercial transport concepts spanning vehicle size classes from regional jet (98 passengers) to very large twin aisle size (400 passengers). Both conventional tube-and-wing (T+W) concepts and unconventional (over-wing-nacelle (OWN), hybrid wing body (HWB), mid-fuselage nacelle (MFN)) concepts were developed. A set of propulsion and airframe technologies were defined and integrated onto these advanced concepts which were then sized to meet the baseline mission requirements. Block fuel burn performance was then estimated, resulting in reductions relative to the 2005 best-in-class baseline performance ranging from 39% to 49%. The advanced single-aisle and large twin aisle T+W concepts had reductions of 43% and 41%, respectively, relative to the 737-800 and 777-200LR aircraft. The single-aisle OWN concept and the large twin aisle class HWB concept had reductions of 45% and 47%, respectively. In addition to their estimated fuel burn reduction performance, these unconventional concepts have the potential to provide significant noise reductions due, in part, to engine shielding provided by the airframe. Finally, all of the advanced concepts also have the potential for significant NOx emissions reductions due to the use of advanced combustor technology. Noise and NOx emissions reduction estimates were also generated for these concepts as part of the ERA project.

Nickol, Craig L.↗
NASA NTRS2016

Emergency Locator Transmitter System Performance During Three Full-Scale General Aviation Crash Tests

Full-scale crash tests were conducted on three Cessna 172 aircraft at NASA Langley Research Center's Landing and Impact Research facility during the summer of 2015. The purpose of the three tests was to evaluate the performance of commercially available Emergency Locator Transmitter (ELT) systems and support development of enhanced installation guidance. ELTs are used to provide location information to Search and Rescue (SAR) organizations in the event of an aviation distress situation, such as a crash. The crash tests simulated three differing severe but survivable crash conditions, in which it is expected that the onboard occupants have a reasonable chance of surviving the accident and would require assistance from SAR personnel. The first simulated an emergency landing onto a rigid surface, while the second and third simulated controlled flight into terrain. Multiple ELT systems were installed on each airplane according to federal regulations. The majority of the ELT systems performed nominally. In the systems which did not activate, post-test disassembly and inspection offered guidance for non-activation cause in some cases, while in others, no specific cause could be found. In a subset of installations purposely disregarding best practice guidelines, failure of the ELT-to-antenna cabling connections were found. Recommendations for enhanced installation guidance of ELT systems will be made to the Radio Technical Commission for Aeronautics (RTCA) Special Committee 229 for consideration for adoption in a future release of ELT minimum operational performance specifications. These recommendations will be based on the data gathered during this test series as well as a larger series of crash simulations using computer models that will be calibrated based on these data

Littell, Justin D.↗
NASA NTRS2016

How Usability Testing Resulted in Improvements to Ground Collision Software for General Aviation: Improved Ground Collision Avoidance System (IGCAS)

The NASA improved Ground Collision Avoidance System (iGCAS) team conducted an onsite usability study at Experimental Aircraft Association (EAA) Air Venture in Oshkosh, Wisconsin from July 19 through July 26, 2015. EAA Air Venture had approximately 550,000 attendees from which the sample pool of pilots were selected. The objectives of this study were to assess the overall appropriateness and acceptability of iGCAS as a warning system for General Aviation aircraft, usability of the iGCAS displays and audio cues, test terrain avoidance characteristics, performance, functionality, pilot response time, and correlate terrain avoidance performance and pilot response time data.

collision avoidance↗
NASA NTRS2016

NASA Environmentally Responsible Aviation's Highly-Loaded Front Block Compressor Demonstration

This presentation will detail the work done to improve thermal efficiency in the compression process of a gas turbine engine for aircraft applications under NASAs Environmentally Responsible Aviation Project. The talk will present the goals and objectives of the work and show the activity of both Phase 1 and Phase 2 tests and analysis. The summary shows the projected fuel burn savings achieved through system studies.

ERA↗
NASA NTRS2016

Workshop on Assurance for Autonomous Systems for Aviation

This report describes the workshop on Assurance for Autonomous Systems for Aviation that was held in January 2016 in conjunction with the SciTech 2016 conference held in San Diego, CA. The workshop explored issues related to assurance for autonomous systems and also the idea of trust in these systems. Specifically, we focused on discussing current practices for assurance of autonomy, identifying barriers specific to autonomy as related to assurance as well as operational scenarios demonstrating the need to address the barriers. Furthermore, attention was given to identifying verification techniques that may be applicable to autonomy, as well as discussing new research directions needed to address barriers, thereby involving potential shifts in current practices.

assurance↗
NASA NTRS2017

Secure Network-Centric Aviation Communication (SNAC)

The existing National Airspace System (NAS) communications capabilities are largely unsecured, are not designed for efficient use of spectrum and collectively are not capable of servicing the future needs of the NAS with the inclusion of new operators in Unmanned Aviation Systems (UAS) or On Demand Mobility (ODM). SNAC will provide a ubiquitous secure, network-based communications architecture that will provide new service capabilities and allow for the migration of current communications to SNAC over time. The necessary change in communication technologies to digital domains will allow for the adoption of security mechanisms, sharing of link technologies, large increase in spectrum utilization, new forms of resilience and redundancy and the possibly of spectrum reuse. SNAC consists of a long term open architectural approach with increasingly capable designs used to steer research and development and enable operating capabilities that run in parallel with current NAS systems.

Communications↗
NASA NTRS2017

Validation of Fatigue Modeling Predictions in Aviation Operations

Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

operational safety↗
NASA NTRS2019

Artificial Intelligence in Aviation Safety Applications - Exploring Myths and Truths of AI and ML

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as ways to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques.Panel speakers will present machine learning approaches they have developed on a variety of aviation data, including digital flight data, safety reporting data, and voice communications data. They will discuss the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel will also discuss common pitfalls in developing an AI solution, the dangers of the current hype around AI, tips for gaining value from a ML solution, how to determine whether a ML approach is appropriate for a problem, and more.

Reeves, Scott (Capt.)↗
NASA NTRS2019

Evaluation Studies of Solid Oxide-Based Fuel Cells for Light-Weight Electrical Power in Aviation: Concepts and Opportunities for the Application of Additive Manufacturing

Currently NASA is investigating the feasibility of hybrid-electric, solid oxide fuel cell power (SOFC) systems for generating electrical power for airborne propulsion and secondary/auxiliary power. As part of this project, researchers are investigating the performance of SOFC hardware in aviation-like environments, to establish the barriers, and potential suitability, of this technology. Immediate findings indicate the unsuitability of current SOFC stack architectures with regards to ease of manufacture, specific energy, as well as environmental and mechanical durability. One possible solution is to create light-weight high specific energy density solid oxide fuel cells. The possibility may be achievable by the use of modern processing techniques such as additive manufacturing. Here I discuss the use additive manufacturing to fabricate ceramic anode, electrolytes, and cathode cells supported on various metal foams with predicted performance based upon variable porosities

Goldsby, Jon C.↗
NASA NTRS2019

Space Radiation and Plasma Effects on Satellites and Aviation: Quantities and Metrics for Tracking Performance of Space Weather Environment Models

The Community Coordinated Modeling Center has been leading communitywide space science and space weather model validation projects for many years. These efforts have been broadened and extended via the newly launched International Forum for Space Weather Modeling Capabilities Assessment (https://ccmc.gsfc.nasa.gov/assessment/). Its objective is to track space weather models' progress and performance over time, a capability that is critically needed in space weather operations and different user communities in general. The Space Radiation and Plasma Effects Working Team of the afore mentioned International Forum works on one of the many focused evaluation topics and deals with five different subtopics (https://ccmc.gsfc.nasa.gov/assessment/topics/radiationall.php) and varieties of particle populations: Surface Charging from tens of eV to 50keV electrons and internal charging due to energetic electrons from hundreds keV to several MeVs. Single event effects from solar energetic particles and galactic cosmic rays (several MeV to TeV), total dose due to accumulation of doses from electrons (>100 keV) and protons (>1 MeV) in a broad energy range, and radiation effects from solar energetic particles and galactic cosmic rays at aviation altitudes. A unique aspect of the Space Radiation and Plasma Effects focus area is that it bridges the space environments, engineering, and user communities. The intent of the paper is to provide an overview of the current status and to suggest a guide for how to best validate space environment models for operational/engineering use, which includes selection of essential space environment and effect quantities and appropriate metrics.

Zheng, Yihua↗
NASA NTRS2019

ARMD Workshop on Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation

This report documents the goals, organization and outcomes of the NASA Aeronautics Research Mission Directorate’s (ARMD) Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation Workshop. The workshop began with a series of plenary presentations by leaders in the field of structures and materials, followed by concurrent symposia focused on forecasting the future of various technologies related to rapid manufacturing of metallic materials and polymeric matrix composites, referred to herein as composites. Shortly after the workshop, questionnaires were sent to key workshop participants from the aerospace industry with requests to rank the importance of a series of potential investment areas identified during the workshop. Outcomes from the workshop and subsequent questionnaires are being used as guidance for NASA investments in this important technology area.

Ransom, Jonathan B.↗
NASA NTRSDate not supplied

Decarbonizing Aviation with Electric Propulsion

NASA’s role in decarbonizing aviation includes the development of flight-weight, flight-ready, safe, and certifiable technologies for regional and single-aisle transport vehicles.

Rodger Dyson↗
NASA NTRSDate not supplied

Creating Formal Characterizations ofRoutine Contingency Management inCommercial Aviation

Traditional approaches to safety management focus on collection of data describing unwanted states (i.e., accidents and incidents) and analysis of undesired behaviors (i.e., faults and errors) that precede those states. Thus, in the traditional view of safety, safety is both defined and measured by its absence, namely the lack of safety. In extremely high confidence systems like commercial air transport, however, opportunities to measure the absence of safety are relatively rare. Ironically, a critical barrier to measuring safety and the impact of mitigation strategies in commercial aviation is the lack of opportunities for measurement.

Intelligent Contingency Management,↗
NASA NTRSDate not supplied

Creating Formal Characterizations of Routine Contingency Management in Commercial Aviation

The identification, modelling, and analysis of root causes of accidents and incidents dominate conventional safety management approaches. However, the effect of humans’ safety-producing behavior on the overall resilience of the system is often neglected. Additionally, emerging aviation markets are giving rise to concepts of operation, such as urban air mobility and optionally piloted air cargo operations, that are leading to a shift in locus of control between humans and automation. Without an understanding of the human contribution to safety, it is difficult to assess the effects of these novel role allocations on overall system safety. In this work, safety-producing behaviors are identified and abstracted into resilient performance strategies. Production rules that encapsulate these strategies are then generated and classified in the Soar cognitive architecture. The strategies are then applied to a remotely-operated air cargo example to demonstrate how safe learning is facilitated. The learned rules and strategies are then formally verified.

Safety Critical Systems↗
NASA NTRSDate not supplied

Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of an potential upcoming adverse event. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with a carefully designed Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture to predict different type of adverse events for any given flights and identify their precursors with little to no post-processing.Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft’s parameters that are correlated to these events. These parameters can be considered precursors to the events.

multiple instance learning↗